van Dijk David, Manor Ohad, Carey Lucas B
Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 76100, Israel.
Department of Genome Sciences, University of Washington, Seattle, WA, 98195, USA.
Curr Biol. 2014 Jun 2;24(11):R516-7. doi: 10.1016/j.cub.2014.04.039.
The number of applicants vastly outnumbers the available academic faculty positions. What makes a successful academic job market candidate is the subject of much current discussion [1-4]. Yet, so far there has been no quantitative analysis of who becomes a principal investigator (PI). We here use a machine-learning approach to predict who becomes a PI, based on data from over 25,000 scientists in PubMed. We show that success in academia is predictable. It depends on the number of publications, the impact factor (IF) of the journals in which those papers are published, and the number of papers that receive more citations than average for the journal in which they were published (citations/IF). However, both the scientist's gender and the rank of their university are also of importance, suggesting that non-publication features play a statistically significant role in the academic hiring process. Our model (www.pipredictor.com) allows anyone to calculate their likelihood of becoming a PI.
申请人的数量远远超过了现有的学术教职岗位数量。如何成为一名成功的学术就业市场候选人是当前众多讨论的主题[1-4]。然而，到目前为止，还没有对谁能成为首席研究员（PI）进行定量分析。我们在此使用机器学习方法，基于来自PubMed中超过25000名科学家的数据来预测谁能成为PI。我们表明，学术界的成功是可预测的。它取决于出版物的数量、发表这些论文的期刊的影响因子（IF），以及获得的引用次数高于其发表期刊平均引用次数的论文数量（引用次数/IF）。然而，科学家的性别及其所在大学的排名也很重要，这表明非发表特征在学术招聘过程中起着统计学上显著的作用。我们的模型（www.pipredictor.com）允许任何人计算自己成为PI的可能性。
